| 基于跨时间尺度迁移学习的污水处理模型漂移校正方法 |
| 摘要点击 1022 全文点击 133 投稿时间：2024-01-18 修订日期：2024-04-04 |
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| 中文关键词 多层感知机神经网络（MLP）模型 机制模型 迁移学习 模型漂移 系统适应性 知识迁移 |
| 英文关键词 multi-layer perceptron neural network （MLP） model mechanism model transfer learning model drift system adaptability knowledge transfer |
| 作者 | 单位 | E-mail | | 申渝 | 重庆工商大学人工智能学院, 智能制造服务国际科技合作基地, 智能感知与区块链技术重庆市重点实验室, 重庆 400067
重庆南向泰斯环保技术研究院有限公司, 重庆 400069 | shenyu@ctbu.edu.cn | | 廖万山 | 重庆工商大学人工智能学院, 智能制造服务国际科技合作基地, 智能感知与区块链技术重庆市重点实验室, 重庆 400067 | | | 李慧敏 | 重庆工商大学人工智能学院, 智能制造服务国际科技合作基地, 智能感知与区块链技术重庆市重点实验室, 重庆 400067
重庆大学环境与生态学院, 重庆 400045 | | | 冯东 | 重庆中法环保研发中心有限公司, 重庆 400010 | | | 郭智威 | 重庆工商大学人工智能学院, 智能制造服务国际科技合作基地, 智能感知与区块链技术重庆市重点实验室, 重庆 400067
重庆南向泰斯环保技术研究院有限公司, 重庆 400069 | | | 张冰 | 重庆工商大学人工智能学院, 智能制造服务国际科技合作基地, 智能感知与区块链技术重庆市重点实验室, 重庆 400067
重庆南向泰斯环保技术研究院有限公司, 重庆 400069 | | | 高旭 | 重庆工商大学人工智能学院, 智能制造服务国际科技合作基地, 智能感知与区块链技术重庆市重点实验室, 重庆 400067
重庆中法环保研发中心有限公司, 重庆 400010 | | | 王建辉 | 重庆工商大学人工智能学院, 智能制造服务国际科技合作基地, 智能感知与区块链技术重庆市重点实验室, 重庆 400067
重庆南向泰斯环保技术研究院有限公司, 重庆 400069 | jhwang@ctbu.edu.cn | | 陈猷鹏 | 重庆大学环境与生态学院, 重庆 400045 | ypchen@cqu.edu.cn |
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| 中文摘要 |
| 数据是智能运维的核心基础,但当前污水厂数据普遍不足,且污水处理系统状态随内外部环境动态演化. 污水厂的智能运维面临着建模难度大,及因系统演化而导致的模型漂移问题. 针对该问题,选取水温、水质和微生物状态等都有显著差异的夏冬两季作为典型对比场景,将机制模型与神经网络结合,建立了基于跨时间尺度迁移学习的污水处理模型漂移校正方法. 首先,针对数据不足问题,建立并校准活性污泥模型（ASM）,以夏季工况数据作为输入,模拟计算运行参数和出水数据,生成模拟运行数据集,实现数据增广和质量提升,用于训练多层感知机神经网络（MLP）模型. 结果显示,MLP模型对夏季出水COD、氨氮和总磷等的平均模拟准确率在95%以上；然后,针对模型在冬季工况中出现模拟准确率大幅下降等模型漂移问题,将冬季实测数据作为目标域数据集,以MLP模型作为预训练模型进行迁移学习. 结果表明,迁移学习后模型性能显著提升,出水COD、氨氮、总氮和总磷的平均模拟准确率分别提高了21.49%、60.79%、58.14%和46.74%. 研究提出的跨时间尺度迁移学习方法,能有效解决模型漂移问题,实现模型对污水处理系统动态演化的跟随响应. |
| 英文摘要 |
| Data is the core foundation of intelligent operation and maintenance, but currently, there is generally insufficient data for wastewater treatment plants, and the status of wastewater treatment systems dynamically evolves with the changes in the internal and external environment. The intelligent operation and maintenance of wastewater plants face difficulties in modeling and model drift caused by system evolution. In response to this issue, the summer and winter seasons with significant differences in wastewater temperature, wastewater quality, and microbial status were selected as typical comparison scenarios. The mechanism model was combined with neural networks to establish a wastewater treatment model drift correction method based on cross-time scale transfer learning. Firstly, in response to the problem of insufficient data, an activated sludge model （ASM） was established and calibrated. Summer operating data was used as input to simulate and calculate operating parameters and effluent data, generating a simulated operating dataset to achieve data augmentation and quality improvement. This was used to train a multi-layer perceptron neural network （MLP） model. The results showed that the average simulation accuracy of the MLP model for summer effluent COD, ammonia nitrogen, total phosphorus, etc., was all over 95%. This indicates the feasibility of training MLP models based on ASM-generated data. Then, the MLP model was used to guide the operation of the pilot A2O project. Experimental data analysis showed that the model drift phenomenon was significant in the field of wastewater treatment. During the operation of the pilot plant guided by the summer model, the accuracy of the predicted values gradually decreased, and the average prediction accuracy of the model for effluent COD gradually decreased from 98.14% to 75.18%. The phenomenon of model drift required effective correction to maximize the effectiveness of the model. In response to the problem of model drift caused by a significant decrease in simulation accuracy in winter operating conditions, a transfer learning approach was introduced. The winter measured data was used as the target domain dataset, and the MLP model was used as the pre-trained model for transfer learning. The experimental results showed that transfer learning methods can significantly improve model performance. After transfer learning, the average simulation accuracy of the MLP model for effluent COD, ammonia nitrogen, total nitrogen, and total phosphorus was relatively improved by 28.58%, 184.44%, 207.56%, and 100.51%, with absolute improvement values of 21.49%, 60.79%, 58.14%, and 46.74%, respectively. This indicates that the cross-time scale transfer learning method proposed in this study can significantly improve model performance, effectively solve model drift problems, and achieve a model-following response to the dynamic evolution of wastewater treatment systems. This study indicates that transfer learning based on pre-trained models only requires a small amount of engineering data and computational complexity to achieve model updating and correction. Compared to model retraining, this method reduces computational complexity and reduces the dependence on engineering data during data-driven model updates. |
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